Data Contextualization: Cross-Discipline Insights Without the Fuss

Big data. You’ve heard all about it since the 1990s. It’s been both the bane of IT’s evolution and the catalyst for so many amazing innovations in digital transformation. Back then, Howard Rheingold wrote forward-thinking books about “mind-expanding technologies.” He was talking about how our intelligence can be amplified with new technologies and, in this context, how powerful cross-discipline collaboration and cooperation can be. Behind it all was the realization that the technology of the day enabled data collection on an unprecedented scale, with the unintended consequence of it outpacing our meager abilities to make sense of it all.

The Virtual Museum of the Frog

An example Rheingold describes in Virtual Reality is the virtual museum of the frog. A properly curated display would not just include a bunch of frogs sitting on a table. It would focus on a distinct species of frog, placed in its natural habitat, while exposing all related flora and fauna. You would get a complete picture of this particular frog, where it lives, how it lives, what it eats, what eats it, how and when it reproduces, and so forth. 

The problems that revealed themselves were the stovepiped data repositories that housed, respectively, great photography and video of the frog, another for the biology of the frog, then the ecology of the frog, weather around the frog-a-verse, what the frogs eat, lifecycle of their food supplies, and so many more. For there to be a meaningful virtual museum of the frog, cooperation among the repository stakeholders and their wares is paramount, argued Howard Rheingold.

What he was saying, in part, is that you can’t know the frog unless you know its context. You have to connect the frog data with its biological, ecological, environmental (etc.) data to show all the right relationships visually. After all, the virtual museum of the frog is visual. You see which plants and insects it eats, which foliage it uses for camouflage, how it swims and hides in water, etc. Such high fidelity visuals of those relationships would not be possible without all that contextual data. To be sure, computer horsepower was not (and is not) the limiting factor for a virtual museum. It was cross-discipline cooperation that was elusive. What incentives are/were there to collaborate? Create such incentives, and you might just get everything you need for your virtual museum.

A way forward.

What if you were able to present limited, frog-specific data to someone who could supply all the contextual data at once, make the connections between elements, and present all that visually? Cross-discipline cooperation on steroids! Further imagine that you could cross-reference things like weather and food supplies with mating habits. “Oh,” you might exclaim, “When the fly population explodes in May and the frogs are well fed after the long winter, they begin their mating rituals. AHA! Severe weather in spring that decimates the fly population affects the frog’s reproduction cycle.”

Given a limited slice of data, Gemini’s solutions automatically supply context from hundreds of reliable sources, making tacit relationships explicit, and empowering you to see those relationships visually. It’s as if all the keepers of disparate frog-related data come together to collaborate. Complete disintermediation. You are able to quickly draw meaningful connections and otherwise hidden conclusions. Gemini’s data contextualization exposes related information (context) to make interpretation intuitive. Like the museum of the frog, you begin to see patterns, trends, correlations, and even causation. It tells a compelling story, which is perhaps the realization of Rheigold’s musings.

Evolution of big data.

From the 1990s to the 2020s, big data has been all the news. According to Forbes, Michael Cox and David Ellsworth coined “big data” in their 1997 paper “Application-controlled demand paging for out-of-core visualization” in the proceedings of the IEEE 8th conference on visualization. Many others tried to quantify the amount of data in the world and how it was expanding. That same year, Michael Lesk predicted, “…in a few years, (a) we will be able [to] save everything–no information will have to be thrown out, and (b) the typical piece of information will never be looked at by a human being.” 

In the May 2012 article  “Critical Questions for Big Data” published in Information, Communications, and Society, big data is defined as “a cultural, technological, and scholarly phenomenon that rests on the interplay of:  

  1. Technology: maximizing computation power and algorithmic accuracy to gather, analyze, link, and compare large data sets. 
  2. Analysis: drawing on large data sets to identify patterns in order to make economic, social, technical, and legal claims. 
  3. Mythology: the widespread belief that large data sets offer a higher form of intelligence and knowledge that can generate insights that were previously impossible, with the aura of truth, objectivity, and accuracy.”

In the following 10 years, technology and analysis techniques have blossomed to become data contextualization and visualization. In a sense, it has turned the mythology of large data sets into reality, but perhaps not the way the “Critical Questions” authors imagined. Gemini Data’s solutions empower organizations to quickly squeeze gems of insight from big (or medium or small) data. All that without the pain of clunky early-adopter offerings. These products are for the majority, totally democratized.

Want to learn more…

… about data contextualization and how it can help your business? Gemini transforms data and analytics by enabling you to intuitively make connections that benefit your business. We can help you effectively transform data into stories that expose, predict, and send you on a successful business journey.

Featured Insights

Gemini Products